A drift detection method based on dynamic classifier selection

• Published in: Data mining and knowledge discovery Vol. 34; no. 1; pp. 50 - 74
• Main Authors: Pinagé, Felipe, dos Santos, Eulanda M., Gama, João
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Classifiers; Computer Science; Data Mining and Knowledge Discovery; Drift; Error detection; Fraud; Information Storage and Retrieval; Intrusion; Machine learning; Methods; Physics; Statistics for Engineering; Upgrading; Data streams; Self-training; Concept drift; Drift detection; Ensemble classifiers

Machine learning algorithms can be applied to several practical problems, such as spam, fraud and intrusion detection, and customer preferences, among others. In most of these problems, data come in streams, which mean that data distribution may change over time, leading to concept drift. The litera...